2、使用lambda表达式+UserDefinedFunction: frompyspark.sqlimportfunctions as F df=df.withColumn('add_column', F.UserDefinedFunction(lambdaobj: int(obj)+2)(df.age)) df.show() ===>> +---+---+---+ |name|age|add_column| +---+---+---+ | p1| 56| 58| | p2| 23| 25| | p3|...
'a', 12) 记录一行数据 Column 记录一列数据并包含列信息(StructField) ''' schema = StructType([StructField("id", LongType(), True) ,StructField("name", StringType(), True) ,StructField("age", IntegerType(), True)]) ''' 也可以: schema = StructType().add('id', LongType...
检查条件并填充另一列ENiterrows(): 按行遍历，将DataFrame的每一行迭代为(index, Series)对，可以通过...
from pyspark.sql import SparkSession # 创建SparkSession对象 spark = SparkSession.builder.getOrCreate() # 创建示例DataFrame data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)] df = spark.createDataFrame(data, ["Name", "Age"]) # 添加新列 df_with_new_column = df.withColumn("Gen...
from pyspark.sql.functions import col # 选择列 df.select(col("column_name")) # 重命名列 df.select(col("column_name").alias("new_column_name")) 2.字符串操作 concat:连接多个字符串。 substring:从字符串中提取子串。 trim:去除字符串两端的空格。
DataFrame column operations withcolumn select when Partitioning and lazy processing cache 计算时间 集群配置 json PYSPARK学习笔记 Defining a schema # Import the pyspark.sql.types library from pyspark.sql.types import * # Define a new schema using the StructType method people_schema = StructType([ # ...
Add the columns folder, filename, width, and height Add split_cols as a column spark 分布式存储 # Don't change this query query = "FROM flights SELECT * LIMIT 10" # Get the first 10 rows of flights flights10 = spark.sql(query) # Show the results flights10.show() 1. 2. 3. 4....
# add a new column data = data.withColumn("newCol",df.oldCol+1) # replace the old column data = data.withColumn("oldCol",newCol) # rename the column data.withColumnRenamed("oldName","newName") # change column data type data.withColumn("oldColumn", data.oldColumn.cast("integer")) (...
from pyspark.sql import Columnfrom pyspark.sql.functions import uppertype(df.c) == type(upper(df.c)) == type(df.c.isNull()) True 上述生成的Column可用于从DataFrame中选择列。例如,DataFrame.select()获取返回另一个DataFrame的列实例: df.select(df.c).show() ...
# put features into a feature vector column assembler = VectorAssembler(inputCols=featureCols, outputCol="features") # Initialize the `standardScaler` standardScaler=StandardScaler(inputCol="features",outputCol="features_scaled") assembled_df = assembler.transform(housing_df) ...